INN Hotels Project

Student - Ashley Graham

Context

A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.

The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.

The cancellation of bookings impact a hotel on various fronts:

• Loss of resources (revenue) when the hotel cannot resell the room.
• Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
• Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
• Human resources to make arrangements for the guests.

Objective

The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Data Description

The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.

Data Dictionary

• Booking_ID: unique identifier of each booking
• no_of_adults: Number of adults
• no_of_children: Number of Children
• no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
• no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
• type_of_meal_plan: Type of meal plan booked by the customer:
  • Not Selected – No meal plan selected
  • Meal Plan 1 – Breakfast
  • Meal Plan 2 – Half board (breakfast and one other meal)
  • Meal Plan 3 – Full board (breakfast, lunch, and dinner)
• required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
• room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
• lead_time: Number of days between the date of booking and the arrival date
• arrival_year: Year of arrival date
• arrival_month: Month of arrival date
• arrival_date: Date of the month
• market_segment_type: Market segment designation.
• repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
• no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
• no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
• avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
• no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
• booking_status: Flag indicating if the booking was canceled or not.

Importing necessary libraries and data

# this will help in making the Python code more structured automatically (good coding practice)
%load_ext nb_black

import warnings

warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning

warnings.simplefilter("ignore", ConvergenceWarning)

# Libraries to help with reading and manipulating data

import pandas as pd
import numpy as np

# Library to split data
from sklearn.model_selection import train_test_split

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)


# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.linear_model import LogisticRegression


# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    plot_confusion_matrix,
    precision_recall_curve,
    roc_curve,
    make_scorer,
)

# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV

Data Overview

• Observations
• Sanity checks

Load the data

# code to load the csv file
INN = pd.read_csv("INNHotelsGroup.csv")

# copying data to another variable to avoid any changes to original data
data = INN.copy()

View the first and last 5 rows of the dataset.

# code to see the first five rows of data
data.head()

	Booking_ID	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	0	1	2	Meal Plan 1	0	Room_Type 1	224	2017	10	2	Offline	0	0	0	65.00	0	Not_Canceled
1	INN00002	2	0	2	3	Not Selected	0	Room_Type 1	5	2018	11	6	Online	0	0	0	106.68	1	Not_Canceled
2	INN00003	1	0	2	1	Meal Plan 1	0	Room_Type 1	1	2018	2	28	Online	0	0	0	60.00	0	Canceled
3	INN00004	2	0	0	2	Meal Plan 1	0	Room_Type 1	211	2018	5	20	Online	0	0	0	100.00	0	Canceled
4	INN00005	2	0	1	1	Not Selected	0	Room_Type 1	48	2018	4	11	Online	0	0	0	94.50	0	Canceled

# code to see the last five rows of data
data.head()

	Booking_ID	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	0	1	2	Meal Plan 1	0	Room_Type 1	224	2017	10	2	Offline	0	0	0	65.00	0	Not_Canceled
1	INN00002	2	0	2	3	Not Selected	0	Room_Type 1	5	2018	11	6	Online	0	0	0	106.68	1	Not_Canceled
2	INN00003	1	0	2	1	Meal Plan 1	0	Room_Type 1	1	2018	2	28	Online	0	0	0	60.00	0	Canceled
3	INN00004	2	0	0	2	Meal Plan 1	0	Room_Type 1	211	2018	5	20	Online	0	0	0	100.00	0	Canceled
4	INN00005	2	0	1	1	Not Selected	0	Room_Type 1	48	2018	4	11	Online	0	0	0	94.50	0	Canceled

Understand the shape of the dataset.

# code to understand the shape of the data
data.shape

(36275, 19)

• The dataset has 36275 rows and 14 columns

Check the data types of the columns for the dataset.

# code to see data type and number of null values
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB

Observation

• We see that the column names have no spaces or strange characters, they appear to be in order.
• There are no missing values in the data set
• There are 14 numeric colums and 5 object type
• We will look at unique values in each column to verify that there are no strange characters in any of the columns

# looking at value counts for non-numeric features

num_to_display = 10  # defining this up here so it's easy to change later if I want
for colname in data.dtypes[data.dtypes == "object"].index:
    val_counts = data[colname].value_counts(dropna=False)  # i want to see NA counts
    print(val_counts[:num_to_display])
    if len(val_counts) > num_to_display:
        print(f"Only displaying first {num_to_display} of {len(val_counts)} values.")
    print("\n\n")  # just for more space between

INN10712    1
INN14258    1
INN00237    1
INN07840    1
INN07373    1
INN30692    1
INN10175    1
INN03773    1
INN28414    1
INN35318    1
Name: Booking_ID, dtype: int64
Only displaying first 10 of 36275 values.



Meal Plan 1     27835
Not Selected     5130
Meal Plan 2      3305
Meal Plan 3         5
Name: type_of_meal_plan, dtype: int64



Room_Type 1    28130
Room_Type 4     6057
Room_Type 6      966
Room_Type 2      692
Room_Type 5      265
Room_Type 7      158
Room_Type 3        7
Name: room_type_reserved, dtype: int64



Online           23214
Offline          10528
Corporate         2017
Complementary      391
Aviation           125
Name: market_segment_type, dtype: int64



Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64

Observations

• Most bookings choose Meal_Plan1 - 76%
• Most bookings choose Room_Type1 - 78%
• Most of the bookings are Online - 64%
• Most of the bookings are Not_Cancelled- 67%

For the remaining 33% of the bookings that are cancelled it will be useful to analyze the segments that have the most cancellations`

# looking at value counts for numeric features

num_to_display = 20  # defining this up here so it's easy to change later if I want
for colname in data.dtypes[data.dtypes == "int64"].index:
    val_counts = data[colname].value_counts(dropna=False)  # i want to see NA counts
    print(val_counts[:num_to_display])
    if len(val_counts) > num_to_display:
        print(f"Only displaying first {num_to_display} of {len(val_counts)} values.")
    print("\n\n")  # just for more space between

2    26108
1     7695
3     2317
0      139
4       16
Name: no_of_adults, dtype: int64



0     33577
1      1618
2      1058
3        19
9         2
10        1
Name: no_of_children, dtype: int64



0    16872
1     9995
2     9071
3      153
4      129
5       34
6       20
7        1
Name: no_of_weekend_nights, dtype: int64



2     11444
1      9488
3      7839
4      2990
0      2387
5      1614
6       189
7       113
10       62
8        62
9        34
11       17
15       10
12        9
14        7
13        5
17        3
16        2
Name: no_of_week_nights, dtype: int64



0    35151
1     1124
Name: required_car_parking_space, dtype: int64



0     1297
1     1078
2      643
3      630
4      628
5      577
6      519
8      436
7      429
12     412
14     384
11     371
37     337
39     335
9      332
13     332
10     317
19     316
18     302
15     298
Name: lead_time, dtype: int64
Only displaying first 20 of 352 values.



2018    29761
2017     6514
Name: arrival_year, dtype: int64



10    5317
9     4611
8     3813
6     3203
12    3021
11    2980
7     2920
4     2736
5     2598
3     2358
2     1704
1     1014
Name: arrival_month, dtype: int64



13    1358
17    1345
2     1331
19    1327
4     1327
16    1306
20    1281
6     1273
15    1273
18    1260
14    1242
30    1216
12    1204
8     1198
29    1190
21    1158
5     1154
26    1146
25    1146
1     1133
Name: arrival_date, dtype: int64
Only displaying first 20 of 31 values.



0    35345
1      930
Name: repeated_guest, dtype: int64



0     35937
1       198
2        46
3        43
11       25
5        11
4        10
13        4
6         1
Name: no_of_previous_cancellations, dtype: int64



0     35463
1       228
2       112
3        80
4        65
5        60
6        36
7        24
8        23
10       19
9        19
11       15
12       12
14        9
15        8
13        7
16        7
20        6
17        6
18        6
Name: no_of_previous_bookings_not_canceled, dtype: int64
Only displaying first 20 of 59 values.



0    19777
1    11373
2     4364
3      675
4       78
5        8
Name: no_of_special_requests, dtype: int64

Overall Observations

• Most of the guest selected Meal Plan 1(27835)- 76%, while 5130 did not select any plan at all.
• Most of the guest booked Room_Type_1 (28130)-78%
• Most of the bookings were online (23214)-64%

• Almost 33% of the books were cancelled (11885)

• Most of the booking were from guests that had no children (33577)-93%

• The majority of bookings were for 2 adults (26108)`-72%
• The majority of weekend nights were for 0 weekend nights - 47%
• 2 weeknights appears to be the most popular booking overall.-32%
• October month has the most arrivals, followed by September
• Only 930 guest were repeated customers, while 35345 were first time guests
• Most of the bookings did not have previous cancellations 35937-97%
• No of previous booking not cancelled = 0 was the largest group in this category (35463)-98%
• Most of the guest did not have any special request (19777)

No_of_previous_cancellations and No_of_previous_bookings_not_canceled appear to carry similar information.

Summary of the dataset

# code to generate the numeric data description with the rows and columns transposed
data.describe().T

	count	mean	std	min	25%	50%	75%	max
no_of_adults	36275.0	1.844962	0.518715	0.0	2.0	2.00	2.0	4.0
no_of_children	36275.0	0.105279	0.402648	0.0	0.0	0.00	0.0	10.0
no_of_weekend_nights	36275.0	0.810724	0.870644	0.0	0.0	1.00	2.0	7.0
no_of_week_nights	36275.0	2.204300	1.410905	0.0	1.0	2.00	3.0	17.0
required_car_parking_space	36275.0	0.030986	0.173281	0.0	0.0	0.00	0.0	1.0
lead_time	36275.0	85.232557	85.930817	0.0	17.0	57.00	126.0	443.0
arrival_year	36275.0	2017.820427	0.383836	2017.0	2018.0	2018.00	2018.0	2018.0
arrival_month	36275.0	7.423653	3.069894	1.0	5.0	8.00	10.0	12.0
arrival_date	36275.0	15.596995	8.740447	1.0	8.0	16.00	23.0	31.0
repeated_guest	36275.0	0.025637	0.158053	0.0	0.0	0.00	0.0	1.0
no_of_previous_cancellations	36275.0	0.023349	0.368331	0.0	0.0	0.00	0.0	13.0
no_of_previous_bookings_not_canceled	36275.0	0.153411	1.754171	0.0	0.0	0.00	0.0	58.0
avg_price_per_room	36275.0	103.423539	35.089424	0.0	80.3	99.45	120.0	540.0
no_of_special_requests	36275.0	0.619655	0.786236	0.0	0.0	0.00	1.0	5.0

Observation

• No _of_adults: On average there were 2 adults per room
• No_of_children : There is a large difference between the minimum no of children and max. There may be outliers present in this variable.
• No_of_weekend_nights : On average occupants stayed one weekend night. There is a large difference between the 75th percentile and max. There may be outliers present in this variable.
• No_of_week_nights : On average occupant stayed two weeknights. There is a large difference between the 75th percentile and the max value. There may be outliers present in the dataset.

Exploratory Data Analysis (EDA)

• EDA is an important part of any project involving data.
• It is important to investigate and understand the data better before building a model with it.
• A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
• A thorough analysis of the data, in addition to the questions mentioned below, should be done.

Questions:

1. What are the busiest months in the hotel?
2. Which market segment do most of the guests come from?
3. Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?
4. What percentage of bookings are canceled?
5. Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?
6. Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?

Univariate Analysis

## code to create a histogram combined with a box plot


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

No_of_Adults

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_adults", bins=100)

Observations

• On average 2 adults booked a room
• This data would be better represented by a categorical plot

No_of_children

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_children", bins=100)

Observations

• Most of the bookings had no children
• This data would be better represented by a categorical plot
• Data appears to have a lot of outliers

No_of_weekend_nights

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_weekend_nights", bins=100)

Observations

• No weekend nights seems to be the most frequent occurence
• This data would be better represented by a categorical plot
• There are outliers in the dataset

No_of_weeknights

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_week_nights", bins=100)

Observations

• Two week nights appear to be the most popular
• This data would be better represented by a categorical plot
• Ther are outliers in the dataset

required_parking_space

# Code to create a histogram box plot
histogram_boxplot(data, "required_car_parking_space", bins=100)

Observations

• The majority of bookings did not require parking space
• This data would be better represented by a categorical plot

Lead_time

# Code to create a histogram box plot
histogram_boxplot(data, "lead_time", bins=100)

Observations

• The data is right skewed with an average lead time of 85 days
• A lot of the bookings appear to have no lead time or just one day lead time
• Most of the bookings appear to have less than a 150 days lead time
• There are a lot of outliers in the data set

Arrival_Year

# Code to create a histogram box plot
histogram_boxplot(data, "arrival_year", bins=100)

Observations

• Most of the arrivals in this data set were in 2018
• This should not be a critical feature in the model since we would be generating a forward looking model
• This data would be better represented by a categorical plot

Arrival_month

# Code to create a histogram box plot
histogram_boxplot(data, "arrival_month", bins=100)

Observations

• October appears to be the most popular month with just over 5000 arrivals
• January appears to be the least popular month for hotel bookings with just around 1000 arrivals
• This data would be better represented by a categorical plot

# Code to create a histogram box plot
histogram_boxplot(data, "arrival_date", bins=100)

Observations

• Arrival_date appers to be uniform
• This data would be better represented by a categorical plot

Repeated_guests

# Code to create a histogram box plot
histogram_boxplot(data, "repeated_guest", bins=50)

Observations

• Most of the guest appear to be first time guests
• This data would be better represented by a categorical plot

No_of_previous_cancellations

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_previous_cancellations", bins=50)

Observations

• Most of the bookings had no previous cancellations
• This data would be better represented by a categorical plot

No _of_previous_bookings_not_cancelled

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_previous_bookings_not_canceled", bins=10)

Average_price_per_room

# Code to create a histogram box plot
histogram_boxplot(data, "avg_price_per_room", bins=100)

No_of_special_requests

# Code to create a histogram box plot
histogram_boxplot(data, "no_of_special_requests", bins=100)

Observations

• Most of the bookings~ 20000 had no special requests
• This data would be better represented by a categorical plot

Converting the data types

# Converting some of the numeric data to categorical since they are discrete variables. 
# Those listed as objects will also be converted to categorical to save on space.
# The conversion to categorical is being done here in preparation for the regression and decision treemodelling to come later on 
data["type_of_meal_plan"].astype("category")
data["room_type_reserved"] = data["room_type_reserved"].astype("category")
data["market_segment_type"] = data["market_segment_type"].astype("category")
data["booking_status"] = data["booking_status"].astype("category")
data["arrival_year"] = data["arrival_year"].astype("category")
data["required_car_parking_space"] = data["required_car_parking_space"].astype("category")
data["no_of_adults"] = data["no_of_adults"].astype("category")
data["no_of_children"] = data["no_of_children"].astype("category")

# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

No_of_adults

# Code to create a labeled bar plot
labeled_barplot(data, "no_of_adults", perc=True)

Observation

• Most of the bookings (72%) were rooms for 2 adults

No_of_children

# Code to create a labeled bar plot
labeled_barplot(data, "no_of_children", perc=True)

Observations

• Most of the bookings (92.6%) were adults not accompanied by children

No_of_weekend_nights

# Code to create a labeled bar plot
labeled_barplot(data, "no_of_weekend_nights", perc=True)

Observations

• Most of the bookings (46.5%) did not have any weekend nights stay.
• 27% of the bookings requested 1 weekend night stay
• 25% of the bookings requested 2 weekend night stays

No_of_week_nights

# Code to create a labeled bar plot
labeled_barplot(data, "no_of_week_nights", perc=True)

Observations

• Most of the bookings required 2 week nights - 31.5%
• This was followed by bookings that required 1 week night stay -26.2%
• 21.6% of the bookings required 3 week night stays

Type_of_meal_plan

# Code to create a labeled bar plot
labeled_barplot(data, "type_of_meal_plan", perc=True)

Observation

• 76.7 % of the bookings selected Meal Plan 1
• 14.1 % of the bookings did not select a meal plan
• 9.1% of the bookings selected Meal plan 3

required_parking_space

# Code to create a labeled bar plot
labeled_barplot(data, "required_car_parking_space", perc=True)

Observation

• 96.9 % of the bookings did not require parking space

room_type_reserved

# Code to create a labeled bar plot
labeled_barplot(data, "room_type_reserved", perc=True)

Observation

• 77.5% of the bookings required Room_Type 1
• 16.7% of the bookings required Room_Type 4

Arrival_year

# Code to create a labeled bar plot
labeled_barplot(data, "arrival_year", perc=True)

Observations

• Most of the bookings in this database 82% were from 2018, while only 18% were from 2017

Arrival_month

# Code to create a labeled bar plot
labeled_barplot(data, "arrival_month", perc=True)

Observations

• Most of the arrivals were in the month of October- 14.7%
• September is the next most poplular month for arrivals - 12.7%
• August was the third busiest month for arrivals - 10.5%

Arrival_date

# Code to create a labeled bar plot
labeled_barplot(data, "arrival_date", perc=True)

Observations

• The arrivals appear to be evenly distributed amongst the days in a month with the exception of the 31st which appears to have only about 1.6%

Market Segment Type

# Code to create a labeled bar plot
labeled_barplot(data, "market_segment_type", perc=True)

Observations

• The majority of bookings are Online at 64%
• This is followed by 29% of the bookings that are made Offline
• The bookings are comprised of 5.6% from Corporate
• Only 0.3 % of the bookings are from Aviation
• Only 1.1% of the bookings are Complimentary

Repeated_guests

# Code to create a labeled bar plot
labeled_barplot(data, "repeated_guest", perc=True)

# code to see the counts of each values in the series
data["repeated_guest"].value_counts()

0    35345
1      930
Name: repeated_guest, dtype: int64

Observations

• Only 2.6% of the bookings 930 are attributed to repeated_guests
• The remaining 97.4% are first timers

No_of_previous_cancellations

# Code to create a labeled bar plot
labeled_barplot(data, "no_of_previous_cancellations", perc=True)

Observations

• 99.1% of the bookings in the system had no previous cancellations.

no_of_special_requests

# Code to create a labeled bar plot
labeled_barplot(data, "no_of_special_requests", perc=True)

Observations

• 54.5% of the bookings had no special requests

• 31.4% of the bookings had 1 special request

• It will be worthwhile investigating later on to see if there are any realtionships between special request and cancellations

Booking Status

# Code to create a labeled bar plot
labeled_barplot(data, "booking_status", perc=True)

data["booking_status"].value_counts()

Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64

Observations

• Of the 36275 bookings 11885 or 32.7% were cancelled

Bivariate analysis

# here we generate a heat map of the independent variables
plt.figure(figsize=(15, 7))
sns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

Observations

• There appears to be a high degree of correlation between some variables such as no_of_previous bookings_not cancelled, no_of_previous_cancellations and repeated_guests
• This will have to be treated prior to developing the models.

Average Room Price vs Market Segment

# let's create a datframe consisting of the Avg_Room_price and market_segment columns
data2 = data[["avg_price_per_room", "market_segment_type"]].copy()

# This code groups the dataframe by the mean of average room price first
data3 = (
    data2.groupby("market_segment_type")[["avg_price_per_room"]]
    .mean()
    .sort_values(by=["avg_price_per_room"], ascending=False, inplace=False)
)

# This code resets the index so that the variable (market segment type) is now an available column to use in further data manipulations such as plots
data4 = data3.reset_index()
data4

	market_segment_type	avg_price_per_room
0	Online	112.256855
1	Aviation	100.704000
2	Offline	91.632679
3	Corporate	82.911740
4	Complementary	3.141765

# Here we are plotting Mean of avg room price vs the Market Segment Type to have a visual of the avg prices across the market segments
plt.figure(figsize=(20, 7))
graph = sns.barplot(y=data4["avg_price_per_room"], x=data4["market_segment_type"])
graph.set_title("Mean of avg room price vs Market Segment Type")
plt.xticks(rotation=70)
for p in graph.patches:
    graph.annotate(
        "%.0f" % p.get_height(),
        (p.get_x() + p.get_width() / 2.0, p.get_height()),
        ha="center",
        va="center",
        fontsize=16,
        color="black",
        xytext=(0, 5),
        textcoords="offset points",
    )
plt.show()

Percentage of repeated guests that cancelled

## Here we find the number of repeated guest that have canceled by applying a filter on the original data set
data9 = data[data["booking_status"] == "Canceled"]
data10 = data9[data9["repeated_guest"] == 1]
data11 = data[data["repeated_guest"] == 1]
percentage = round((len(data10["repeated_guest"]) / len(data11["repeated_guest"])) * 100, 2)
print("The percentage of repeated guest that canceled is :", percentage,"%")

The percentage of repeated guest that canceled is : 1.72 %

Observations

• Of the 930 repeated guests, only 16 or 1.72% have cancelled their bookings

# Now we will convert no of repeated guest to categorical since these values are discrete 0 or 1 before outlier treatment
data["repeated_guest"] = data["repeated_guest"].astype("category")

No_of_special_request in Booking Status = 'Cancelled'

# Code to create a labeled bar plot
labeled_barplot(data9, "no_of_special_requests", perc=True)

Observations

• Of the 11885 cancelled bookings;
• 71.9% consists of bookings that have no special requests attached to them.
• 22.7% consists of bookings that have 1 special requests attached to them.
• 5.4% consists of booking that have 2 special requests attached to them.

Cancellation by Market Sector

# Code to create a labeled bar plot
labeled_barplot(data9, "market_segment_type", perc=True)

Cancellations by Market Segment and Special Request vs Arrival Month

# data9 was filtered to show only cancelled bookings 
plt.figure(figsize=(15, 12))
graph = sns.boxplot(
    y="arrival_month",
    x="no_of_special_requests",
    hue="market_segment_type",
    data=data9)  
graph.set_title("Cancellations by Market Segment and Special Request vs Arrival Month")
plt.xticks(rotation=70)
for p in graph.patches:
    graph.annotate(
        "%.0f" % p.get_height(),
        (p.get_x() + p.get_width() / 2.0, p.get_height()),
        ha="center",
        va="center",
        fontsize=16,
        color="black",
        xytext=(0, 5),
        textcoords="offset points",
    )
plt.show()    
    

Observations

• The group with "No_special_request" has the most cancellations
• Online cancellations are also present whether its's 0, 1 or 2 special requests.

# data9 was filtered to show only cancelled bookings
plt.figure(figsize=(15, 7))
graph = sns.boxplot(y="lead_time", x="market_segment_type", hue=None, data=data9)
graph.set_title("Cancellations by Lead_time and Market_Segment")
plt.xticks(rotation=70)
for p in graph.patches:
    graph.annotate(
        "%.0f" % p.get_height(),
        (p.get_x() + p.get_width() / 2.0, p.get_height()),
        ha="center",
        va="center",
        fontsize=16,
        color="black",
        xytext=(0, 5),
        textcoords="offset points",
    )
plt.show()

Observations

• Of all the bookings cancelled Aviation had the shortest lead time of just a couple of days
• Online cancellations had a mean of around a 100 days
• Offline cancellations had a mean of just over 200 days
• Corporate cancellations appear to be somewhere around 25 days
• Policy for cancellation penalties and refunds will have to take lead time into consideration.

Data Preprocessing

• Missing value treatment (if needed)
• Feature engineering (if needed)
• Outlier detection and treatment (if needed)
• Preparing data for modeling
• Any other preprocessing steps (if needed)

Missing Values

# let's look for missing values
data.isnull().sum()

Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64

Observation

• There are no missing values

Duplicate value check

# looking at which values are duplicated
duplicate = data[data.duplicated()]
print("Duplicated Rows:")
duplicate.sum()

Duplicated Rows:

Booking_ID                              0.0
no_of_weekend_nights                    0.0
no_of_week_nights                       0.0
type_of_meal_plan                       0.0
lead_time                               0.0
arrival_month                           0.0
arrival_date                            0.0
no_of_previous_cancellations            0.0
no_of_previous_bookings_not_canceled    0.0
avg_price_per_room                      0.0
no_of_special_requests                  0.0
dtype: float64

Observation

• There are no duplicated rows

Outlier detection using box plots

numerical_col = data.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

Observation

• No_of_adults and avg_room_price have bothupper and lower`outliers.
• Arrival_date doesn't have any outliers.
• Arrival_year has lower outliers
• all the others have upper outliers

Treating Outliers

# functions to treat outliers by flooring and capping


def treat_outliers(df, col):
    """
    Treats outliers in a variable

    df: dataframe
    col: dataframe column
    """
    Q1 = df[col].quantile(0.25)  # 25th quantile
    Q3 = df[col].quantile(0.75)  # 75th quantile
    IQR = Q3 - Q1
    Lower_Whisker = Q1 - 1.5 * IQR
    Upper_Whisker = Q3 + 1.5 * IQR

    # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker
    # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker
    df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)

    return df


def treat_outliers_all(df, col_list):
    """
    Treat outliers in a list of variables

    df: dataframe
    col_list: list of dataframe columns
    """
    for c in col_list:
        df = treat_outliers(df, c)

    return df

numerical_col = data.select_dtypes(include=np.number).columns.tolist()
data = treat_outliers_all(data, numerical_col)

# let's look at box plot to see if outliers have been treated or not
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

EDA

• It is a good idea to explore the data once again after manipulating it.

Average Price Per Room

# code to create a histogram box plot
histogram_boxplot(data, "avg_price_per_room", bins=20)

Observations

• With outliers removed , the scale appears a bit clearer.
• The avg price per room appearto be around 100 euros.

Lead time

# code to create a histogram box plot
histogram_boxplot(data, "lead_time", bins=100)

Observation

• Lead time is still right skewed with the mean less than the median value.
• Mean lead time appears to be around 85 days
• The majority of the lead time appears to be less than 150 days
• There are a lot of lead times less than around 15 days

Arrival Month vs No of Special requests vs Booking Status

# code to create a box plot
plt.figure(figsize=(15, 12))
sns.boxplot(
    y="arrival_month", x="no_of_special_requests", hue="booking_status", data=data,
)
plt.show()

Observations

• Bookings with 0, 1 or 2 special requests appears to have a lot of cancellations with 0 special request having the most.
• With 0 special requests, cancellations occur from May to September
• With1 special request, cancellations occur from June to October
• With 2 special request, cancellations occur from August to October

• There are no cancellations with bookings having 3 special request

• There is an obvious trend, the more special request there are , the less cancellations occur.

• Cancellations also occur from as early as May when there are no special requests.

Data Preparation

Encoding Not_Canceled as 0 and Canceled as 1

## Code to encode values in the booking_status column. Not_Canceled will be encoded as 0 while Canceled will be encoded as 1
data["booking_status"] = data["booking_status"].apply(
    lambda x: 1 if x == "Canceled" else 0
)

# verification check to ensure that "booking_status" is encoded properly,
data["booking_status"]

0        0
1        0
2        1
3        1
4        1
        ..
36270    0
36271    1
36272    0
36273    1
36274    0
Name: booking_status, Length: 36275, dtype: category
Categories (2, int64): [1, 0]

# Code to fix some spaces in the type_of_meal_plan column
data["type_of_meal_plan"] = [col.replace(" ", "_") for col in data["type_of_meal_plan"]]
data.columns = [col.replace("/", "_") for col in data.columns]
data.columns = [col.replace("-", "_") for col in data.columns]

data.drop(["Booking_ID"], axis=1)

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
0	2	0	1	2	Meal_Plan_1	0	Room_Type 1	224.0	2017	10	2	Offline	0	0	0	65.00	0.0	0
1	2	0	2	3	Not_Selected	0	Room_Type 1	5.0	2018	11	6	Online	0	0	0	106.68	1.0	0
2	1	0	2	1	Meal_Plan_1	0	Room_Type 1	1.0	2018	2	28	Online	0	0	0	60.00	0.0	1
3	2	0	0	2	Meal_Plan_1	0	Room_Type 1	211.0	2018	5	20	Online	0	0	0	100.00	0.0	1
4	2	0	1	1	Not_Selected	0	Room_Type 1	48.0	2018	4	11	Online	0	0	0	94.50	0.0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36270	3	0	2	6	Meal_Plan_1	0	Room_Type 4	85.0	2018	8	3	Online	0	0	0	167.80	1.0	0
36271	2	0	1	3	Meal_Plan_1	0	Room_Type 1	228.0	2018	10	17	Online	0	0	0	90.95	2.0	1
36272	2	0	2	6	Meal_Plan_1	0	Room_Type 1	148.0	2018	7	1	Online	0	0	0	98.39	2.0	0
36273	2	0	0	3	Not_Selected	0	Room_Type 1	63.0	2018	4	21	Online	0	0	0	94.50	0.0	1
36274	2	0	1	2	Meal_Plan_1	0	Room_Type 1	207.0	2018	12	30	Offline	0	0	0	161.67	0.0	0

36275 rows × 18 columns

Split Data

X = data.drop(["booking_status", "Booking_ID"], axis=1)
Y = data["booking_status"]

X = pd.get_dummies(X, drop_first=True)

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)

print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (25392, 34)
Shape of test set :  (10883, 34)
Percentage of classes in training set:
0    0.670644
1    0.329356
Name: booking_status, dtype: float64
Percentage of classes in test set:
0    0.676376
1    0.323624
Name: booking_status, dtype: float64

# Here we look at the first 5 rows of data
X_train.head()

	no_of_weekend_nights	no_of_week_nights	lead_time	arrival_month	arrival_date	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	no_of_adults_1	no_of_adults_2	no_of_adults_3	no_of_adults_4	no_of_children_1	no_of_children_2	no_of_children_3	no_of_children_9	no_of_children_10	type_of_meal_plan_Meal_Plan_2	type_of_meal_plan_Meal_Plan_3	type_of_meal_plan_Not_Selected	required_car_parking_space_1	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	arrival_year_2018	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online	repeated_guest_1
13662	0	1	163.0	10	15	0	0	115.00	0.0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0
26641	0	3	113.0	3	31	0	0	78.15	1.0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0
17835	2	3	289.5	10	14	0	0	78.00	1.0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0
21485	0	3	136.0	6	29	0	0	85.50	0.0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0
5670	1	2	21.0	8	15	0	0	151.00	0.0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0

Building a Model

Model evaluation criterion

Model can make wrong predictions as:

1. Predicting a booking status is at risk of being Canceled but in reality the booking will be a Not_Canceled.
2. Predicting a booking status will be Not_Canceled but in reality the booking will be a Canceled

Which case is more important?

• Both the cases are important as:

• If we predict a booking status is at risk of being Canceled

   - we will be deploying human resources to convince these people to stay via follow up calls, emails and 
   - offering incentives to people that don't need convincing e.g. discount coupons    

• If we predict a booking status will be Not_Canceled but in reality it will be Canceled there will be loss of revenue via

  - Loss of revenue when the hotel cannot resell the room 
  - Being forced to reduced the room prices at the last minute to get them booked 
  - Additional cost of distribution channels by increasing the commissions or paying for publicity to help sell these                rooms.
  - Human resources to help sell these rooms

How to reduce this loss i.e need to reduce False Negatives?

• Recall should be maximized, the greater the Recall score higher the chances of reducing false negatives.

First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.

• The model_performance_classification_statsmodels function will be used to check the model performance of models.
• The confusion_matrix_statsmodels function will be used to plot confusion matrix.

# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf

# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

Logistic Regression (with Sklearn library)

# There are different solvers available in Sklearn logistic regression
# The newton-cg solver is faster for high-dimensional data

lg = LogisticRegression(solver="newton-cg", random_state=1)
model = lg.fit(X_train, y_train)

Checking model performance on training set

# predicting on training set
y_pred_train = lg.predict(X_train)

print("Training set performance:")
print("Accuracy:", accuracy_score(y_train, y_pred_train))
print("Precision:", precision_score(y_train, y_pred_train))
print("Recall:", recall_score(y_train, y_pred_train))
print("F1:", f1_score(y_train, y_pred_train))

Training set performance:
Accuracy: 0.8043478260869565
Precision: 0.7359933268455443
Recall: 0.6330264259237116
F1: 0.6806376960658267

# predicting on the test set
y_pred_test = lg.predict(X_test)

print("Test set performance:")
print("Accuracy:", accuracy_score(y_test, y_pred_test))
print("Precision:", precision_score(y_test, y_pred_test))
print("Recall:", recall_score(y_test, y_pred_test))
print("F1:", f1_score(y_test, y_pred_test))

Test set performance:
Accuracy: 0.8051088854176238
Precision: 0.7295968534906588
Recall: 0.6320272572402045
F1: 0.6773162939297125

Observations

• The training and testing f1_scores are 0.67.

• f1_score on the train and test sets are comparable.

• This shows that the model is showing generalised results.

We have build a logistic regression model which shows good performance on the train and test sets but to identify significant variables we will have to build a logistic regression model using the statsmodels library.

• We will now perform logistic regression using statsmodels, a Python module that provides functions for the estimation of many statistical models, as well as for conducting statistical tests, and statistical data exploration.

• Using statsmodels, we will be able to check the statistical validity of our model - identify the significant predictors from p-values that we get for each predictor variable.

Information on VIF

• Variance Inflation factor: Variance inflation factors measure the inflation in the variances of the regression coefficients estimates due to collinearities that exist among the predictors. It is a measure of how much the variance of the estimated regression coefficient βk is “inflated”by the existence of correlation among the predictor variables in the model.

• General Rule of thumb: If VIF is 1 then there is no correlation among the kth predictor and the remaining predictor variables, and hence the variance of β̂k is not inflated at all. Whereas if VIF exceeds 5, we say there is moderate VIF and if it is 10 or exceeding 10, it shows signs of high multi-collinearity. But the purpose of the analysis should dictate which threshold to use.

Checking Multicollinearity

• In order to make statistical inferences from a logistic regression model, it is important to ensure that there is no multicollinearity present in the data.

# code to check multicollinearity using variance inflation factor calculations
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("Series before feature selection: \n\n{}\n".format(vif_series))

Series before feature selection: 

no_of_weekend_nights                      1.981449
no_of_week_nights                         4.187612
lead_time                                 2.852895
arrival_month                             8.542290
arrival_date                              4.196274
no_of_previous_cancellations                   NaN
no_of_previous_bookings_not_canceled           NaN
avg_price_per_room                       23.362218
no_of_special_requests                    2.093220
no_of_adults_1                           39.000854
no_of_adults_2                          134.662999
no_of_adults_3                           14.144420
no_of_adults_4                            1.157834
no_of_children_1                          1.128298
no_of_children_2                          3.332683
no_of_children_3                          1.040794
no_of_children_9                          1.002322
no_of_children_10                         1.000652
type_of_meal_plan_Meal_Plan_2             1.399317
type_of_meal_plan_Meal_Plan_3             1.026860
type_of_meal_plan_Not_Selected            1.497166
required_car_parking_space_1              1.074415
room_type_reserved_Room_Type 2            1.241947
room_type_reserved_Room_Type 3            1.003559
room_type_reserved_Room_Type 4            1.746760
room_type_reserved_Room_Type 5            1.036397
room_type_reserved_Room_Type 6            3.020687
room_type_reserved_Room_Type 7            1.208170
arrival_year_2018                         7.772960
market_segment_type_Complementary         2.975427
market_segment_type_Corporate            10.792540
market_segment_type_Offline              52.074457
market_segment_type_Online              116.262937
repeated_guest_1                          1.366866
dtype: float64

Removing no_of_previous_cancellations

# Here we are dropping "no_of_previous_cancellations" and renaming series
X_train1 = X_train.drop("no_of_previous_cancellations", axis=1)
vif_series2 = pd.Series(
    [variance_inflation_factor(X_train1.values, i) for i in range(X_train1.shape[1])],
    index=X_train1.columns,
)
print("Series before feature selection: \n\n{}\n".format(vif_series2))

Series before feature selection: 

no_of_weekend_nights                      1.981449
no_of_week_nights                         4.187612
lead_time                                 2.852895
arrival_month                             8.542290
arrival_date                              4.196274
no_of_previous_bookings_not_canceled           NaN
avg_price_per_room                       23.362218
no_of_special_requests                    2.093220
no_of_adults_1                           39.000854
no_of_adults_2                          134.662999
no_of_adults_3                           14.144420
no_of_adults_4                            1.157834
no_of_children_1                          1.128298
no_of_children_2                          3.332683
no_of_children_3                          1.040794
no_of_children_9                          1.002322
no_of_children_10                         1.000652
type_of_meal_plan_Meal_Plan_2             1.399317
type_of_meal_plan_Meal_Plan_3             1.026860
type_of_meal_plan_Not_Selected            1.497166
required_car_parking_space_1              1.074415
room_type_reserved_Room_Type 2            1.241947
room_type_reserved_Room_Type 3            1.003559
room_type_reserved_Room_Type 4            1.746760
room_type_reserved_Room_Type 5            1.036397
room_type_reserved_Room_Type 6            3.020687
room_type_reserved_Room_Type 7            1.208170
arrival_year_2018                         7.772960
market_segment_type_Complementary         2.975427
market_segment_type_Corporate            10.792540
market_segment_type_Offline              52.074457
market_segment_type_Online              116.262937
repeated_guest_1                          1.366866
dtype: float64

# Here we are dropping "no_of_previous_bookings_ not_cancelled" and renaming series
X_train2 = X_train1.drop("no_of_previous_bookings_not_canceled", axis=1)
vif_series3 = pd.Series(
    [variance_inflation_factor(X_train2.values, i) for i in range(X_train2.shape[1])],
    index=X_train2.columns,
)
print("Series before feature selection: \n\n{}\n".format(vif_series3))

Series before feature selection: 

no_of_weekend_nights                   1.981449
no_of_week_nights                      4.187612
lead_time                              2.852895
arrival_month                          8.542290
arrival_date                           4.196274
avg_price_per_room                    23.362218
no_of_special_requests                 2.093220
no_of_adults_1                        39.000854
no_of_adults_2                       134.662999
no_of_adults_3                        14.144420
no_of_adults_4                         1.157834
no_of_children_1                       1.128298
no_of_children_2                       3.332683
no_of_children_3                       1.040794
no_of_children_9                       1.002322
no_of_children_10                      1.000652
type_of_meal_plan_Meal_Plan_2          1.399317
type_of_meal_plan_Meal_Plan_3          1.026860
type_of_meal_plan_Not_Selected         1.497166
required_car_parking_space_1           1.074415
room_type_reserved_Room_Type 2         1.241947
room_type_reserved_Room_Type 3         1.003559
room_type_reserved_Room_Type 4         1.746760
room_type_reserved_Room_Type 5         1.036397
room_type_reserved_Room_Type 6         3.020687
room_type_reserved_Room_Type 7         1.208170
arrival_year_2018                      7.772960
market_segment_type_Complementary      2.975427
market_segment_type_Corporate         10.792540
market_segment_type_Offline           52.074457
market_segment_type_Online           116.262937
repeated_guest_1                       1.366866
dtype: float64

Observation

• Dropping no_of_previous_cancellations and no_of_previous_bookings_not_cancelled removed most of the multicollinearity from the variables.
• We will start the Statsmodels by dropping these colums along with Booking ID

Building a Logistic Regression (with statsmodels library)

# for the data set we will drop "Booking_ID" since this is not an important independent variable.
# we will also drop "no_of_previous_bookings_not_canceled" and "no_of_previous_cancellations" since they are highly correlated.

X = data.drop(
    [
        "booking_status",
        "Booking_ID",
        "no_of_previous_bookings_not_canceled",
        "no_of_previous_cancellations",
    ],
    axis=1,
)
Y = data["booking_status"]

X = pd.get_dummies(X, drop_first=True)

# adding constant
X = sm.add_constant(X)

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)

# fitting logistic regression model
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit(disp=False)

print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25359
Method:                           MLE   Df Model:                           32
Date:                Fri, 19 Nov 2021   Pseudo R-squ.:                  0.3292
Time:                        14:19:17   Log-Likelihood:                -10795.
converged:                      False   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
=====================================================================================================
                                        coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------------------------
const                                -2.6675      0.432     -6.176      0.000      -3.514      -1.821
no_of_weekend_nights                  0.1103      0.020      5.607      0.000       0.072       0.149
no_of_week_nights                     0.0095      0.014      0.700      0.484      -0.017       0.036
lead_time                             0.0164      0.000     59.742      0.000       0.016       0.017
arrival_month                        -0.0414      0.007     -6.361      0.000      -0.054      -0.029
arrival_date                          0.0007      0.002      0.381      0.703      -0.003       0.005
avg_price_per_room                    0.0206      0.001     25.539      0.000       0.019       0.022
no_of_special_requests               -1.4906      0.030    -49.240      0.000      -1.550      -1.431
no_of_adults_1                       -0.2828      0.339     -0.834      0.404      -0.947       0.382
no_of_adults_2                       -0.0311      0.336     -0.092      0.926      -0.689       0.627
no_of_adults_3                       -0.2247      0.349     -0.645      0.519      -0.908       0.458
no_of_adults_4                       -0.5724      1.043     -0.549      0.583      -2.616       1.471
no_of_children_1                      0.0520      0.087      0.599      0.549      -0.118       0.222
no_of_children_2                      0.3120      0.193      1.621      0.105      -0.065       0.689
no_of_children_3                     -0.0275      0.825     -0.033      0.973      -1.644       1.589
no_of_children_9                      2.8864      1.488      1.940      0.052      -0.030       5.803
no_of_children_10                   -13.2723   2224.721     -0.006      0.995   -4373.646    4347.101
type_of_meal_plan_Meal_Plan_2         0.2162      0.066      3.287      0.001       0.087       0.345
type_of_meal_plan_Meal_Plan_3        11.3902    171.869      0.066      0.947    -325.468     348.248
type_of_meal_plan_Not_Selected        0.2744      0.054      5.115      0.000       0.169       0.380
required_car_parking_space_1         -1.6180      0.138    -11.692      0.000      -1.889      -1.347
room_type_reserved_Room_Type 2       -0.4489      0.144     -3.113      0.002      -0.732      -0.166
room_type_reserved_Room_Type 3       -0.1014      1.322     -0.077      0.939      -2.693       2.490
room_type_reserved_Room_Type 4       -0.2474      0.055     -4.498      0.000      -0.355      -0.140
room_type_reserved_Room_Type 5       -0.7036      0.209     -3.370      0.001      -1.113      -0.294
room_type_reserved_Room_Type 6       -0.7488      0.193     -3.874      0.000      -1.128      -0.370
room_type_reserved_Room_Type 7       -0.6128      0.302     -2.027      0.043      -1.205      -0.020
arrival_year_2018                     0.4915      0.060      8.184      0.000       0.374       0.609
market_segment_type_Complementary   -31.5555   6.98e+04     -0.000      1.000   -1.37e+05    1.37e+05
market_segment_type_Corporate        -1.2678      0.263     -4.820      0.000      -1.783      -0.752
market_segment_type_Offline          -2.3049      0.252     -9.148      0.000      -2.799      -1.811
market_segment_type_Online           -0.5288      0.249     -2.126      0.034      -1.016      -0.041
repeated_guest_1                     -1.9228      0.378     -5.092      0.000      -2.663      -1.183
=====================================================================================================

print("Training performance:")
model_performance_classification_statsmodels(lg, X_train, y_train)

Training performance:

	Accuracy	Recall	Precision	F1
0	0.80486	0.6347	0.736404	0.68178

• Some variables such as 'no_of_week_nights' and 'arrival_date
• In the case of 'no_of_adults' all the attributes have a high p-value which means it is not significant therefore we can drop the complete variable.

• For other attributes present in the data, the p-values are high only for few dummy variables and since only one (or some) of the categorical levels have a high p-value we will drop them iteratively as sometimes p-values change after dropping a variable. So, we'll not drop all variables at once.

• Instead, we will do the following repeatedly using a loop:

  • Build a model, check the p-values of the variables, and drop the column with the highest p-value.
  • Create a new model without the dropped feature, check the p-values of the variables, and drop the column with the highest p-value.
  • Repeat the above two steps till there are no columns with p-value > 0.05.

Note: The above process can also be done manually by picking one variable at a time that has a high p-value, dropping it, and building a model again. But that might be a little tedious and using a loop will be more efficient.

# running a loop to drop variables with high p-value

# initial list of columns
cols = X_train.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    X_train_aux = X_train[cols]

    # fitting the model
    model = sm.Logit(y_train, X_train_aux).fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = max(p_values)

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break

selected_features = cols
print(selected_features)

['const', 'no_of_weekend_nights', 'lead_time', 'arrival_month', 'avg_price_per_room', 'no_of_special_requests', 'no_of_adults_1', 'no_of_adults_3', 'type_of_meal_plan_Meal_Plan_2', 'type_of_meal_plan_Not_Selected', 'required_car_parking_space_1', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'arrival_year_2018', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'repeated_guest_1']

# we create a new variable X_train1 to represent the independent varibles that were treated for multicollinearity
X_train1 = X_train[selected_features]

# fitting logistic regression model
logit2 = sm.Logit(y_train, X_train1.astype(float))
lg2 = logit2.fit(disp=False)

print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25372
Method:                           MLE   Df Model:                           19
Date:                Fri, 19 Nov 2021   Pseudo R-squ.:                  0.3279
Time:                        14:19:20   Log-Likelihood:                -10815.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                             -3.2435      0.106    -30.487      0.000      -3.452      -3.035
no_of_weekend_nights               0.1158      0.020      5.930      0.000       0.078       0.154
lead_time                          0.0164      0.000     60.667      0.000       0.016       0.017
arrival_month                     -0.0421      0.006     -6.511      0.000      -0.055      -0.029
avg_price_per_room                 0.0211      0.001     26.981      0.000       0.020       0.023
no_of_special_requests            -1.4898      0.030    -49.417      0.000      -1.549      -1.431
no_of_adults_1                    -0.2487      0.047     -5.323      0.000      -0.340      -0.157
no_of_adults_3                    -0.2208      0.077     -2.886      0.004      -0.371      -0.071
type_of_meal_plan_Meal_Plan_2      0.2021      0.066      3.084      0.002       0.074       0.331
type_of_meal_plan_Not_Selected     0.2739      0.053      5.168      0.000       0.170       0.378
required_car_parking_space_1      -1.6208      0.138    -11.726      0.000      -1.892      -1.350
room_type_reserved_Room_Type 2    -0.3438      0.127     -2.706      0.007      -0.593      -0.095
room_type_reserved_Room_Type 4    -0.2434      0.054     -4.489      0.000      -0.350      -0.137
room_type_reserved_Room_Type 5    -0.7012      0.207     -3.381      0.001      -1.108      -0.295
room_type_reserved_Room_Type 6    -0.5196      0.114     -4.572      0.000      -0.742      -0.297
room_type_reserved_Room_Type 7    -0.5778      0.271     -2.133      0.033      -1.109      -0.047
arrival_year_2018                  0.4899      0.060      8.195      0.000       0.373       0.607
market_segment_type_Corporate     -0.7349      0.104     -7.064      0.000      -0.939      -0.531
market_segment_type_Offline       -1.7725      0.052    -34.003      0.000      -1.875      -1.670
repeated_guest_1                  -1.9181      0.377     -5.090      0.000      -2.657      -1.179
==================================================================================================

• No significant change in the model performance.

Now no feature has p-value greater than 0.05, so we'll consider the features in X_train2 as the final ones and lg2 as final model.

Coefficient interpretations

• Coefficient of some attributes such as (lead time, type of meal plan_not selected, having 1 or 2 children) are positive and an increase in these will lead to increase in chances of a booking being "Canceled"

• Coefficient of some attributes such as (arrival month, no of special request, room types 5,6 or 7, meal plan 2) are negative and an increase in these will lead to decrease in chances of a booking being "Canceled".

Converting coefficients to odds

• The coefficients of the logistic regression model are in terms of log(odd), to find the odds we have to take the exponential of the coefficients.
• Therefore, odds = exp(b)
• The percentage change in odds is given as odds = (exp(b) - 1) * 100

# converting coefficients to odds
odds = np.exp(lg2.params)

# finding the percentage change
perc_change_odds = (np.exp(lg2.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns).T

	const	no_of_weekend_nights	lead_time	arrival_month	avg_price_per_room	no_of_special_requests	no_of_adults_1	no_of_adults_3	type_of_meal_plan_Meal_Plan_2	type_of_meal_plan_Not_Selected	required_car_parking_space_1	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	arrival_year_2018	market_segment_type_Corporate	market_segment_type_Offline	repeated_guest_1
Odds	0.039026	1.122776	1.01658	0.958742	1.021285	0.225419	0.779833	0.801880	1.224011	1.315042	0.197738	0.709096	0.783951	0.495987	0.594758	0.561158	1.632146	0.479531	0.169909	0.146884
Change_odd%	-96.097382	12.277600	1.65795	-4.125811	2.128512	-77.458088	-22.016746	-19.811969	22.401118	31.504152	-80.226241	-29.090384	-21.604947	-50.401307	-40.524219	-43.884197	63.214636	-52.046885	-83.009087	-85.311589

Coefficient interpretations

Factors increasing the chance of a booking being cancelled

• arrival_year_2018: This feature increases the chance of a Cancellationthemost, in fact the odds are 63% or 1.63 times. However sinceyearis aone offoccurence, we will ignore it. Plus the model wil be used to predict the risk offuturebookings beingCancelled`.
• type_of_meal_plan_Not_Selected : Ignoring arrival_year, This feature increases the chance of a Cancellation the most. A 1 unit increase in type_of_meal_plan_Not_Selected will increase the odds of having a Cancellation by a factor of 1.32 or a 31.5% chance of a booking being Cancelled
• no_of_weekend_nights: Holding all other features constant a 1 unit increase in no_of_weekend_nights will increase the odds of a booking being Cancelled by 1.12 times or a 12.3% increase in the odds of having a Cancellation.
• type_of_meal_plan_2 : Holding all other features constant a 1 unit increase in type_of_Meal_Plan_2 will increase the odds of a booking being Cancelled by 1.22 or a 22.4% increase in the odds of having a Cancellation.
• lead_time : Holding all other features constant a 1 unit increase in lead_time will increase the odds of having a Cancellation by 1 or a 1.66% increase in the odds of having a Cancellation.

Factors decreasing the chance of a booking being cancelled

• repeated_guest: This feature decreases the chance of a Cancellation the most. A 1 unit increase in repeated_guests will decrease the odds of having a Cancellation by a factor of 0.15 or a 85.5% chance of a booking being Cancelled
• market_segment_type_Offline: This feature will decrease the odds a booking being Cancelled by a factor of 0.17 or 83%.
• market_segment_type_Corporate : This feature will decrease the odds of a booking beingCancelledby a factor of 0.47 or 52% decrease in the odds of having a a bookingCanceled`
• no_of_special_requests :Holding all other features constant, a 1 unit increase in no_of_special_requests will decrease the odds of having a Cancellation by a factor of 0.23 or a 77% chance of a booking not being cancelled.
• required_car_parking_space: Holding all other features constant a 1 unit increase in this feature will decrease the odds of a booking being Canceled by 0.20 or a 80.2% decrease in the odds of having a Cancellation.

Similar interpretations can be done for the other attributes.

Model Performance Evaluation

Checking model performance on the training set

# creating confusion matrix
confusion_matrix_statsmodels(lg2, X_train1, y_train)

log_reg_model_train_perf = model_performance_classification_statsmodels(
    lg2, X_train1, y_train
)

print("Training performance:")
log_reg_model_train_perf

Training performance:

	Accuracy	Recall	Precision	F1
0	0.804112	0.633505	0.735119	0.680539

ROC-AUC

• ROC-AUC on training set

logit_roc_auc_train = roc_auc_score(y_train, lg2.predict(X_train1))
fpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

• Logistic Regression model is giving a good performance on training set.

Model Performance Improvement

• Let's see if the Recall can be improved further, by changing the model threshold using AUC-ROC Curve.

Optimal threshold using AUC-ROC curve

# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg2.predict(X_train1))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.37293538490213085

Checking model performance on training set

# creating confusion matrix
confusion_matrix_statsmodels(
    lg2, X_train1, y_train, threshold=optimal_threshold_auc_roc
)

# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg2, X_train1, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc

Training performance:

	Accuracy	Recall	Precision	F1
0	0.791076	0.729882	0.667104	0.697082

• Recall has increased significantly from 0.63 to 0.73 (Reduction in False Negatives- Which is what we want)
• Precision has decreased from 0.74 to o.67 (Reduction in True Positives)..Increase in False positives which is not revenue impacting as a False Negative.
• F1 has increased from 0.68 to 0.69 (Minor)
• There was no measurable change in accuracy from a value of 0.8
• The model performance has improved

Let's use Precision-Recall curve and see if we can find a better threshold

y_scores = lg2.predict(X_train1)  # calculate y_scores based on X_train1 series
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()

• At the threshold of 0.41, we get balanced recall and precision.

# setting the threshold
optimal_threshold_curve = 0.41

Checking model performance on training set

# creating confusion matrix
confusion_matrix_statsmodels(lg2, X_train1, y_train, threshold=optimal_threshold_curve)

# here we calculate the training scores based on the statsmodel and X_train1 series using the optimal threshold as defined by precision-recall
log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    lg2, X_train1, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:

	Accuracy	Recall	Precision	F1
0	0.796865	0.700825	0.688153	0.694431

• Model performed better using Optimal Threshold set by the ROC -AUC curve

• Using Optimal Threshold set using Precision Recall curve

  • Recall decreased slightly from 0.73 to 0.70. This is an increase in False Negatives which is revenue impacting.
  • Precision increased slightly from 0.61 to 0.69
  • F1 score decreased slightly from 0.68 to 0.69

• Model using the Optimal Threshold set by the ROC-AUC will be the logistic regression model of choice.

Model Performance Summary

# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression sklearn",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.41 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Logistic Regression sklearn	Logistic Regression-0.37 Threshold	Logistic Regression-0.41 Threshold
Accuracy	0.804112	0.791076	0.796865
Recall	0.633505	0.729882	0.700825
Precision	0.735119	0.667104	0.688153
F1	0.680539	0.697082	0.694431

Let's check the performance on the test set

Dropping the columns from the test set that were dropped from the training set

# Dropping the columns from the test set that were dropped from the training set
X_test1 = X_test[list(X_train1.columns)]

Using model with default threshold

# creating confusion matrix
confusion_matrix_statsmodels(lg2, X_test1, y_test)

## here we calculate the test scores based on the statsmodel and X_train1 series using the default threshold
log_reg_model_test_perf = model_performance_classification_statsmodels(
    lg2, X_test1, y_test
)

print("Test performance:")
log_reg_model_test_perf

Test performance:

	Accuracy	Recall	Precision	F1
0	0.804558	0.633163	0.727569	0.677091

• ROC curve on test set

# Here we are plotting the ROC curve
logit_roc_auc_train = roc_auc_score(y_test, lg2.predict(X_test1))
fpr, tpr, thresholds = roc_curve(y_test, lg2.predict(X_test1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

Using model with threshold=0.37

# creating confusion matrix
confusion_matrix_statsmodels(lg2, X_test1, y_test, threshold=optimal_threshold_auc_roc)

# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg2, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc

Test performance:

	Accuracy	Recall	Precision	F1
0	0.796472	0.737649	0.668038	0.70112

Using model with threshold=0.41

# creating confusion matrix
confusion_matrix_statsmodels(lg2, X_test1, y_test, threshold=optimal_threshold_curve)

# here we calculate the test scores based on the statsmodel and X_test1 series using the optimal threshold based on precision-recall
log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg2, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:

	Accuracy	Recall	Precision	F1
0	0.798585	0.702726	0.683702	0.693083

Final Model Summary

# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression Default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.41 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Logistic Regression Default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.41 Threshold
Accuracy	0.804112	0.791076	0.796865
Recall	0.633505	0.729882	0.700825
Precision	0.735119	0.667104	0.688153
F1	0.680539	0.697082	0.694431

# testing performance comparison

models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression Default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.41 Threshold",
]

print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:

	Logistic Regression Default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.41 Threshold
Accuracy	0.804558	0.796472	0.798585
Recall	0.633163	0.737649	0.702726
Precision	0.727569	0.668038	0.683702
F1	0.677091	0.701120	0.693083

Conclusion

• We have been able to build a predictive model that can be used by the hotel management to determine bookings that are likely to be cancelled.

• The logistic regression model with threshold based on ROC curve has the highest Recall of 0.74

• All the logistic regression models have given a generalized performance on the training and test set.

• Coefficient of [no_of_weekend_nights, no_of_week_nights, lead_time, type_of_meal_plan_Not_Selected] are positive an increase in these will lead to increase in chances of a booking being Cancelled.

• Coefficient of [type_of_meal_plan2, no_of_special_requests, required_car_parking_space] are negative increase in these will lead to decrease in chances of a booking being Cancelled.

Building a Decision Tree model

Split Data

# We will use the data with Multicollinearity since decision trees are not affected by this.
X = data.drop(["booking_status", "Booking_ID"], axis=1)
Y = data["booking_status"]

X = pd.get_dummies(X, drop_first=True)

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)
print(X_train.shape, X_test.shape)

(25392, 34) (10883, 34)

print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 25392
Number of rows in test data = 10883

print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Percentage of classes in training set:
0    0.670644
1    0.329356
Name: booking_status, dtype: float64
Percentage of classes in test set:
0    0.676376
1    0.323624
Name: booking_status, dtype: float64

Model evaluation criterion

Model can make wrong predictions as:

1. Predicting a booking status is at risk of being Canceled but in reality the booking will be a Not_Canceled.
2. Predicting a booking status will be Not_Canceled but in reality the booking will be a Canceled

Which case is more important?

• Both the cases are important as:

• If we predict a booking status is at risk of being Canceled

   - we will be deploying human resources to convince these people to stay via follow up calls, emails and 
   - offering incentives to people that don't need convincing e.g. discount coupons    

• If we predict a booking status will be Not_Canceled but in reality it will be Canceled there will be loss of revenue via

  - Loss of revenue when the hotel cannot resell the room 
  - Being forced to reduced the room prices at the last minute to get them booked 
  - Additional cost of distribution channels by increasing the commissions or paying for publicity to help sell these                rooms.
  - Human resources to help sell these rooms

How to reduce this loss i.e need to reduce False Negatives?

• Recall should be maximized, the greater the Recall score higher the chances of reducing false negatives.

First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.

• The get_recall_score function will be used to check the model performance of models.
• The make_confusion_matrix function will be used to plot confusion matrix.

##  Function to calculate recall score
def get_recall_score(model, predictors, target):
    """
    model: classifier
    predictors: independent variables
    target: dependent variable
    
    """
    prediction = model.predict(predictors)
    return recall_score(target, prediction)

def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

Build the Tree

• We will build our model using the DecisionTreeClassifier function. Using default 'gini' criteria to split.

• If the frequency of class A is 10% and the frequency of class B is 90%, then class B will become the dominant class and the decision tree will become biased toward the dominant classes.

• In this case, we can pass a dictionary {0:0.33,1:0.67} to the model to specify the weight of each class and the decision tree will give more weightage to class 1.

• class_weight is a hyperparameter for the decision tree classifier.

# Here we set the parameters for building the default tree
model = DecisionTreeClassifier(
    criterion="gini", class_weight={0: 0.33, 1: 0.67}, random_state=1
)

# fittig the training data to the tree
model.fit(X_train, y_train)

DecisionTreeClassifier(class_weight={0: 0.33, 1: 0.67}, random_state=1)

Checking model performance on training set

# creating the confusion matrix
confusion_matrix_sklearn(model, X_train, y_train)

# calculating the recall score on training data for the original tree
decision_tree_perf_train = get_recall_score(model, X_train, y_train)

print("Recall Score:", decision_tree_perf_train)

Recall Score: 0.9950974530670812

• Model is able to perfectly classify all the data points on the training set.
• 0 errors on the training set, each sample has been classified correctly.
• As we know a decision tree will continue to grow and classify each data point correctly if no restrictions are applied as the trees will learn all the patterns in the training set.
• This generally leads to overfitting of the model as Decision Tree will perform well on the training set but will fail to replicate the performance on the test set.

Checking model performance on test set

# creating the confuion matrix
confusion_matrix_sklearn(model, X_test, y_test)

decision_tree_perf_test = get_recall_score(model, X_test, y_test)
print("Recall Score:", decision_tree_perf_test)

Recall Score: 0.8038046564452016

• There is a small disparity in performance of model on training and test set although the Recall Score on the test data is very good at0.8.

Visualizing the Decision Tree

## creating a list of column names
feature_names = X_train.columns.to_list()

# plotting the decision tree
plt.figure(figsize=(20, 30))
out = tree.plot_tree(
    model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(model, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 179.47
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.50
|   |   |   |   |   |   |   |   |   |--- repeated_guest_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 48.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.79, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  48.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- repeated_guest_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [35.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 115.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  115.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.36] class: 1
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [16.83, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- weights: [513.15, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  179.47
|   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |--- lead_time <= 18.00
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  18.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 10.72] class: 1
|   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 63.29
|   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [18.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 10.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.75
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.75
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 44.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  44.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  63.29
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.91, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.70] class: 1
|   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [45.87, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 46.35
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  46.35
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.98
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.93, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 88.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.13, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  88.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 73.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  73.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |--- avg_price_per_room >  99.98
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [3.96, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 132.43
|   |   |   |   |   |   |   |   |   |--- lead_time <= 81.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 123.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  123.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  81.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 122.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  122.22
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  132.43
|   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- repeated_guest_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- repeated_guest_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [13.86, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [14.19, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.37] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.68
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.68
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- lead_time <= 107.50
|   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.63, 4.02] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  107.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 116.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  116.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.36] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 125.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  125.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 102.09
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 114.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.44
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.44
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 50.25] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  114.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  102.09
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 101.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.37] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  101.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [14.85, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.25
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 31.49] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.25
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 146.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.32, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  146.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.67] class: 1
|   |   |   |   |   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [46.20, 0.00] class: 0
|   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 103.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  103.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [18.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 67.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  67.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.32, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.29, 0.67] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 144.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  144.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.67] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |--- weights: [23.76, 0.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 99.44
|   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |--- weights: [40.92, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.05
|   |   |   |   |   |   |   |   |   |--- weights: [13.86, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.05
|   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.71] class: 1
|   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.66
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [52.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.66
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.10
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.10
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.28, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.69] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  99.44
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.62, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [12.54, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.68] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |--- no_of_adults_3 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- no_of_adults_3 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 119.25
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  119.25
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults_3 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults_3 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 143.83
|   |   |   |   |   |   |   |   |   |--- weights: [9.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  143.83
|   |   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.93, 0.00] class: 0
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- required_car_parking_space_1 <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 51.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 29.04
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  29.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  51.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.62, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.63, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.38
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.34
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 102.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  102.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.34
|   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.45
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_3 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_3 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.45
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.75
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.69] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 28.14] class: 1
|   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 104.31
|   |   |   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.26, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- avg_price_per_room >  104.31
|   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 144.76
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  144.76
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.06
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 22.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  22.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.06
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |--- required_car_parking_space_1 >  0.50
|   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |--- weights: [21.45, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [279.84, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.91, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [14.19, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 95.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  95.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.55
|   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.32, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.55
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [5.94, 0.00] class: 0
|   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.67] class: 1
|   |   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 67.65
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  67.65
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.68] class: 1
|   |   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 83.39
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  83.39
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 141.25
|   |   |   |   |   |   |   |   |   |--- lead_time <= 143.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.59, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  143.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  141.25
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |--- weights: [18.81, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 157.64
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [26.73, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [22.77, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  157.64
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 158.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  158.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.39
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.28, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.39
|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.12
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.86, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.12
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 172.65
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  172.65
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 139.57
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [32.01, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  139.57
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 140.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  140.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 126.33
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.92, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  126.33
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space_1 <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 118.55
|   |   |   |   |   |   |   |--- lead_time <= 61.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [31.02, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [56.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- lead_time >  61.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 66.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.93
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.93
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |--- avg_price_per_room >  118.55
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.98
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.98
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.15
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 121.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  121.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 55.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  55.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [22.11, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |--- required_car_parking_space_1 >  0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |--- weights: [59.40, 0.00] class: 0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |--- weights: [60.06, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |--- weights: [641.52, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [13.86, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.06
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.06
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 20.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  20.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.89, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 80.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  80.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- no_of_adults_3 <= 0.50
|   |   |   |   |   |   |   |--- weights: [19.47, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults_3 >  0.50
|   |   |   |   |   |   |   |--- weights: [3.63, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 98.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  98.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 107.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  107.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  21.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 103.50
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  103.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.12
|   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.12
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.42
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.42
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 164.25
|   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  164.25
|   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |--- lead_time <= 106.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  106.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |--- weights: [29.70, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |--- avg_price_per_room <= 82.47
|   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- weights: [0.00, 88.44] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.87
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.69] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.87
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.15] class: 1
|   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.92, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.36] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 28.57
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  28.57
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [18.81, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  82.47
|   |   |   |   |   |--- no_of_adults_3 <= 0.50
|   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 225.12] class: 1
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.69] class: 1
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.38] class: 1
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 7.37] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |   |   |--- no_of_adults_3 >  0.50
|   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |--- weights: [1.32, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.67] class: 1
|   |   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 10.05] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.13, 4.02] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.85
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.85
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.03] class: 1
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 278.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 57.29
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  57.29
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  278.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.91, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |--- lead_time <= 283.25
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  283.25
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.69] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.67] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 35.22
|   |   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |   |--- weights: [3.63, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |   |--- arrival_date <= 11.00
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  11.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |--- avg_price_per_room >  35.22
|   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |--- weights: [0.00, 32.83] class: 1
|   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- arrival_date <= 4.00
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  4.00
|   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_date <= 12.00
|   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  12.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.68] class: 1
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |--- lead_time <= 176.50
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  176.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |--- no_of_children_2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [15.51, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_children_2 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 172.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  172.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 279.75
|   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  279.75
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.67] class: 1
|   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 83.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 272.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 226.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  226.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  272.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.10
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- weights: [3.96, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 76.48
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |   |--- lead_time <= 245.50
|   |   |   |   |   |   |   |   |   |--- weights: [15.18, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  245.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  76.48
|   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 233.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 152.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  152.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |--- lead_time >  233.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.81
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.81
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 208.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  208.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 234.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 175.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  175.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.70] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  234.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 269.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 176.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  176.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  269.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |--- arrival_date <= 3.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  3.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.43
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.43
|   |   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 55.92
|   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  55.92
|   |   |   |   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.19
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.19
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |--- lead_time <= 284.25
|   |   |   |   |   |   |   |--- arrival_date <= 30.00
|   |   |   |   |   |   |   |   |--- weights: [39.27, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  30.00
|   |   |   |   |   |   |   |   |--- lead_time <= 168.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  168.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  284.25
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [4.29, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 84.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  84.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 0.67] class: 1
|   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |--- lead_time <= 167.00
|   |   |   |   |   |   |   |--- weights: [0.33, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  167.00
|   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |--- weights: [0.00, 326.29] class: 1
|   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |--- weights: [0.00, 1086.07] class: 1
|   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [15.51, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- lead_time <= 172.50
|   |   |   |   |   |   |--- arrival_date <= 28.00
|   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  28.00
|   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |--- lead_time >  172.50
|   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |--- weights: [0.00, 6.03] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.68] class: 1

# importance of features in the tree building ( The importance of a feature is computed as the
# (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(
    pd.DataFrame(
        model.feature_importances_, columns=["Imp"], index=X_train.columns
    ).sort_values(by="Imp", ascending=False)
)

                                           Imp
lead_time                             0.351017
avg_price_per_room                    0.141231
market_segment_type_Online            0.094474
no_of_special_requests                0.086353
arrival_date                          0.084464
arrival_month                         0.064144
no_of_week_nights                     0.048612
no_of_weekend_nights                  0.035611
no_of_adults_1                        0.018386
arrival_year_2018                     0.015615
market_segment_type_Offline           0.009879
no_of_adults_2                        0.008857
type_of_meal_plan_Not_Selected        0.008368
required_car_parking_space_1          0.007468
room_type_reserved_Room_Type 4        0.005837
no_of_children_1                      0.003426
room_type_reserved_Room_Type 2        0.003219
type_of_meal_plan_Meal_Plan_2         0.002965
no_of_adults_3                        0.001968
no_of_children_2                      0.001756
repeated_guest_1                      0.001584
room_type_reserved_Room_Type 6        0.001529
market_segment_type_Corporate         0.001475
room_type_reserved_Room_Type 5        0.001032
room_type_reserved_Room_Type 7        0.000362
no_of_adults_4                        0.000204
no_of_children_3                      0.000161
type_of_meal_plan_Meal_Plan_3         0.000000
no_of_children_9                      0.000000
room_type_reserved_Room_Type 3        0.000000
market_segment_type_Complementary     0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
no_of_children_10                     0.000000

importances = model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

• According to the decsion tree model. Lead time is the most importatnt variable for predicting if a booking will be Canceled

Do we need to prune the tree?

The decsion tree created is very complex so we will reduce overfitting via two methods Hyperparameter tuning and Cost Complexity Pruning

Reducing over fitting

Using GridSearch for Hyperparameter tuning of our tree model

• Hyperparameter tuning is also tricky in the sense that there is no direct way to calculate how a change in the hyperparameter value will reduce the loss of your model, so we usually resort to experimentation. i.e we'll use Grid search
• Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters.
• It is an exhaustive search that is performed on a the specific parameter values of a model.
• The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-search over a parameter grid.

# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight={0: 0.33, 1: 0.67})

# Grid of parameters to choose from
parameters = {
    "max_depth": [5, 10, 15],
    "criterion": ["entropy", "gini"],
    "splitter": ["best", "random"],
    "min_impurity_decrease": [0.00001, 0.0001, 0.01],
}

# Type of scoring used to compare parameter combinations
scorer = make_scorer(recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(class_weight={0: 0.33, 1: 0.67}, max_depth=15,
                       min_impurity_decrease=0.0001, random_state=1)

Checking performance on training set

# creating the confusion matrix
confusion_matrix_sklearn(estimator, X_train, y_train)

decision_tree_tune_perf_train = get_recall_score(estimator, X_train, y_train)
print("Recall Score:", decision_tree_tune_perf_train)

Recall Score: 0.9048188449121128

Checking performance on test set

# creating the confusion matrix
confusion_matrix_sklearn(estimator, X_test, y_test)

decision_tree_tune_perf_test = get_recall_score(estimator, X_test, y_test)
print("Recall Score:", decision_tree_tune_perf_test)

Recall Score: 0.8657013060760931

• The model is giving generalized results now. The Recall score has improved on the test data.

Visualizing the Decision Tree

# plotting the decision tree
plt.figure(figsize=(15, 10))
out = tree.plot_tree(
    estimator,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 179.47
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.50
|   |   |   |   |   |   |   |   |   |--- weights: [91.74, 4.69] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 3.35] class: 1
|   |   |   |   |   |   |   |--- lead_time >  16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.57, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.36] class: 1
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- weights: [529.98, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  179.47
|   |   |   |   |   |   |--- weights: [1.32, 11.39] class: 1
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- lead_time <= 68.50
|   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 63.29
|   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [18.48, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 1.34] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.75
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 5.36] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.60, 0.67] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.75
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 44.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 26.13] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  44.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  63.29
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [136.62, 16.75] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.91, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 6.70] class: 1
|   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |--- weights: [182.82, 12.06] class: 0
|   |   |   |   |   |--- lead_time >  68.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.98
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.93, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.64, 11.39] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- weights: [33.99, 5.36] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  99.98
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [3.96, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 132.43
|   |   |   |   |   |   |   |   |   |--- weights: [4.29, 54.27] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  132.43
|   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 52.26] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [13.86, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.60, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.63, 2.68] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.64, 4.02] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- weights: [26.40, 1.34] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.37] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.68
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 8.04] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.68
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.62, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.47, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- lead_time <= 107.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.29, 4.02] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  107.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.97, 13.40] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- weights: [8.91, 2.68] class: 0
|   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 102.09
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.65, 63.65] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  102.09
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 7.37] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [14.85, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.32, 33.50] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 1.34] class: 0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.33, 1.34] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |--- weights: [49.17, 0.67] class: 0
|   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |--- weights: [21.12, 0.67] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [11.55, 8.04] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.26, 35.51] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.28, 4.02] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.61, 0.67] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 144.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  144.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 2.68] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 2.01] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |--- weights: [26.07, 0.67] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 99.44
|   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |--- weights: [40.92, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.05
|   |   |   |   |   |   |   |   |   |--- weights: [13.86, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.05
|   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.16, 0.67] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.71] class: 1
|   |   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.21
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 1.34] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.21
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.98, 4.69] class: 1
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |--- weights: [68.64, 2.68] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.69] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  99.44
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [21.12, 13.40] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.62, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [64.35, 10.05] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.68] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |--- weights: [7.26, 11.39] class: 1
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- weights: [27.06, 102.51] class: 1
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [11.55, 0.67] class: 0
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.30, 16.08] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.93, 0.00] class: 0
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- required_car_parking_space_1 <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |   |--- weights: [22.44, 3.35] class: 0
|   |   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 6.70] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.62, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |   |--- weights: [9.24, 2.68] class: 0
|   |   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.34
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [12.21, 42.88] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 102.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  102.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.61, 1.34] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.34
|   |   |   |   |   |   |   |   |   |--- weights: [4.95, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.45
|   |   |   |   |   |   |   |   |   |--- weights: [35.31, 4.02] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.45
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 5.36] class: 1
|   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.26, 20.77] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal_Plan_2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 28.14] class: 1
|   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 104.31
|   |   |   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.26, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.16, 52.26] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.49, 81.74] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [32.67, 181.57] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  104.31
|   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [144.54, 957.43] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 4.02] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.06
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 22.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  22.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.59, 36.85] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.06
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.61, 2.68] class: 0
|   |   |   |   |--- required_car_parking_space_1 >  0.50
|   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |--- weights: [21.45, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |--- weights: [308.55, 3.35] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |   |--- weights: [6.93, 0.67] class: 0
|   |   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |   |--- weights: [0.00, 3.35] class: 1
|   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |   |--- weights: [0.33, 2.68] class: 1
|   |   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 83.39
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.98, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 4.02] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  83.39
|   |   |   |   |   |   |   |   |--- weights: [10.23, 2.01] class: 0
|   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |--- weights: [18.81, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |--- weights: [0.99, 1.34] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |--- weights: [220.11, 18.09] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |--- weights: [0.33, 1.34] class: 1
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.39
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.59, 0.67] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.39
|   |   |   |   |   |   |   |   |   |   |--- weights: [18.15, 13.40] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.33, 2.01] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |--- weights: [14.85, 0.67] class: 0
|   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [54.78, 4.02] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not_Selected >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 126.33
|   |   |   |   |   |   |   |   |   |--- weights: [14.52, 1.34] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  126.33
|   |   |   |   |   |   |   |   |   |--- weights: [4.29, 6.03] class: 1
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space_1 <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 118.55
|   |   |   |   |   |   |   |--- lead_time <= 61.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [31.02, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [56.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |--- lead_time >  61.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.32, 25.46] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 66.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  66.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.50, 24.12] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.93
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [24.09, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.93
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |--- avg_price_per_room >  118.55
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.98
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.98
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.15
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 121.20
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.25, 2.68] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  121.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 55.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  55.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.28, 4.69] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [108.24, 146.73] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [22.11, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 8.04] class: 1
|   |   |   |   |   |--- required_car_parking_space_1 >  0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |--- weights: [59.40, 0.00] class: 0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [701.58, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |--- weights: [14.19, 0.67] class: 0
|   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.61, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [21.78, 16.75] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [18.48, 5.36] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [19.80, 2.68] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- weights: [23.10, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.66, 4.02] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.33, 1.34] class: 1
|   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [61.71, 4.69] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 1.34] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_children_1 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 107.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  107.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.62, 4.69] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.93, 5.36] class: 0
|   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |--- weights: [0.66, 3.35] class: 1
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [38.94, 45.56] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |--- weights: [5.61, 1.34] class: 0
|   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |--- weights: [29.70, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults_1 <= 0.50
|   |   |   |   |--- avg_price_per_room <= 82.47
|   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |--- weights: [1.32, 126.63] class: 1
|   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.92, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.95, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.36] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [33.33, 5.36] class: 0
|   |   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |   |--- arrival_year_2018 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.22, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year_2018 >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.95, 116.58] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.30, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [20.79, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  82.47
|   |   |   |   |   |--- no_of_adults_3 <= 0.50
|   |   |   |   |   |   |--- weights: [10.56, 458.95] class: 1
|   |   |   |   |   |--- no_of_adults_3 >  0.50
|   |   |   |   |   |   |--- weights: [2.31, 0.00] class: 0
|   |   |   |--- no_of_adults_1 >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |--- weights: [1.32, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |--- weights: [0.33, 10.72] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [20.79, 4.02] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.03] class: 1
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.34] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 278.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 57.29
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  57.29
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [63.03, 1.34] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  278.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [25.08, 6.70] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |--- weights: [0.99, 5.36] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 35.22
|   |   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |   |--- weights: [3.63, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |   |--- weights: [0.33, 2.01] class: 1
|   |   |   |   |   |--- avg_price_per_room >  35.22
|   |   |   |   |   |   |--- weights: [0.33, 36.18] class: 1
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- weights: [2.64, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- weights: [0.66, 3.35] class: 1
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |--- weights: [0.66, 1.34] class: 1
|   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |--- weights: [15.84, 0.67] class: 0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 44.12
|   |   |   |   |   |   |   |   |--- weights: [0.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  44.12
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 83.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.64, 7.37] class: 1
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- no_of_adults_2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |--- no_of_adults_2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [5.61, 0.67] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- weights: [3.96, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 76.48
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |   |--- weights: [20.79, 1.34] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 0.67] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  76.48
|   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 233.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 152.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.66, 2.01] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  152.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  233.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.23, 6.70] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children_2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.96, 7.37] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.99, 6.70] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 269.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 176.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.99, 3.35] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  176.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  269.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.01] class: 1
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |--- weights: [3.63, 1.34] class: 0
|   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 55.92
|   |   |   |   |   |   |   |   |--- weights: [0.99, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  55.92
|   |   |   |   |   |   |   |   |--- weights: [3.96, 14.07] class: 1
|   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |--- weights: [49.83, 3.35] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |--- weights: [0.00, 1412.36] class: 1
|   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |--- weights: [10.23, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [15.51, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |--- weights: [1.65, 0.00] class: 0
|   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |--- weights: [1.65, 10.05] class: 1

Observations from the tree

• If a booking is not made online made with a lead time of less than 74.5 days there is a very high probability that the booking will Not_Cancel

Interpretations from other decision rules can be made similarly.

# importance of features in the tree building ( The importance of a feature is computed as the
# (normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print(
    pd.DataFrame(
        estimator.feature_importances_, columns=["Imp"], index=X_train.columns
    ).sort_values(by="Imp", ascending=False)
)

# Here we will see that importance of features has increased

                                           Imp
lead_time                             0.391994
market_segment_type_Online            0.134938
no_of_special_requests                0.124633
avg_price_per_room                    0.116906
arrival_month                         0.059427
arrival_date                          0.033705
no_of_weekend_nights                  0.027518
no_of_week_nights                     0.027200
no_of_adults_1                        0.020433
arrival_year_2018                     0.019914
market_segment_type_Offline           0.013002
required_car_parking_space_1          0.010391
no_of_adults_2                        0.006363
type_of_meal_plan_Not_Selected        0.005112
no_of_adults_3                        0.001498
room_type_reserved_Room_Type 2        0.001465
room_type_reserved_Room_Type 4        0.001202
market_segment_type_Corporate         0.000843
no_of_children_1                      0.000758
type_of_meal_plan_Meal_Plan_2         0.000684
room_type_reserved_Room_Type 5        0.000597
room_type_reserved_Room_Type 6        0.000369
repeated_guest_1                      0.000353
room_type_reserved_Room_Type 7        0.000348
no_of_children_2                      0.000345
room_type_reserved_Room_Type 3        0.000000
type_of_meal_plan_Meal_Plan_3         0.000000
no_of_children_9                      0.000000
no_of_children_3                      0.000000
market_segment_type_Complementary     0.000000
no_of_adults_4                        0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
no_of_children_10                     0.000000

# plotting the important features
importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

• In tuned decision tree Leadtime is the most important feature followed by market_segment_type_Online.

Cost Complexity Pruning

The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tree. In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned. Here we only show the effect of ccp_alpha on regularizing the trees and how to choose a ccp_alpha based on validation scores.

Total impurity of leaves vs effective alphas of pruned tree

Minimal cost complexity pruning recursively finds the node with the "weakest link". The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first. To get an idea of what values of ccp_alpha could be appropriate, scikit-learn provides DecisionTreeClassifier.cost_complexity_pruning_path that returns the effective alphas and the corresponding total leaf impurities at each step of the pruning process. As alpha increases, more of the tree is pruned, which increases the total impurity of its leaves.

# setting the parameters of the cost complexity pruner
clf = DecisionTreeClassifier(random_state=1, class_weight={0: 0.33, 1: 0.67})
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities

# creating a dataframe for the ccp_alphas vs impurities
pd.DataFrame(path)

	ccp_alphas	impurities
0	0.000000e+00	0.008376
1	0.000000e+00	0.008376
2	1.958732e-20	0.008376
3	1.958732e-20	0.008376
4	1.958732e-20	0.008376
...	...	...
1685	8.886770e-03	0.328000
1686	9.831603e-03	0.337832
1687	1.273174e-02	0.350564
1688	3.410153e-02	0.418767
1689	8.123211e-02	0.499999

1690 rows × 2 columns

# plotting total impurity vs effective alphas
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()

Next, we train a decision tree using the effective alphas. The last value in ccp_alphas is the alpha value that prunes the whole tree, leaving the tree, clfs[-1], with one node.

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight={0: 0.33, 1: 0.67}
    )
    clf.fit(X_train, y_train)
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.08123211203866026

For the remainder, we remove the last element in clfs and ccp_alphas, because it is the trivial tree with only one node. Here we show that the number of nodes and tree depth decreases as alpha increases.

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

recall_train = []  # creating the empty list for the values
for clf in clfs:  # for loop to append the list
    pred_train = clf.predict(X_train)
    values_train = recall_score(y_train, pred_train)
    recall_train.append(values_train)

recall_test = []  # creating the empty list for the values
for clf in clfs:  # for loop to append the list
    pred_test = clf.predict(X_test)
    values_test = recall_score(y_test, pred_test)
    recall_test.append(values_test)

# calculation of the test and training scores
train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]

fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(
    ccp_alphas, recall_train, marker="o", label="train", drawstyle="steps-post",
)
ax.plot(ccp_alphas, recall_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()

Maximum value of Recall is at 0.0001 alpha, but if we choose this, the decision tree will be too complex , instead we can choose alpha 0.006 to simplify the tree still get a higher recall.

# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=0.00011408531261741304,
                       class_weight={0: 0.33, 1: 0.67}, random_state=1)

best_model.fit(X_train, y_train)

DecisionTreeClassifier(ccp_alpha=0.00011408531261741304,
                       class_weight={0: 0.33, 1: 0.67}, random_state=1)

checking performance on training set

confusion_matrix_sklearn(best_model, X_train, y_train)

print("Recall Score:", get_recall_score(best_model, X_train, y_train))

Recall Score: 0.9194069113954323

checking performance on test set

confusion_matrix_sklearn(best_model, X_test, y_test)

print("Recall Score:", get_recall_score(best_model, X_test, y_test))

Recall Score: 0.862862010221465

Visualizing the Decision Tree

# code to plot the decision tree
plt.figure(figsize=(5, 5))

out = tree.plot_tree(
    best_model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

• This model might be giving the highest recall but it is too complex and a business may not be able to use it.

Creating a model with 0.006 alpha

# setting ccp_alpha = 0.006
best_model2 = DecisionTreeClassifier(
    ccp_alpha=0.006, class_weight={0: 0.33, 1: 0.67}, random_state=1
)
best_model2.fit(X_train, y_train)

DecisionTreeClassifier(ccp_alpha=0.006, class_weight={0: 0.33, 1: 0.67},
                       random_state=1)

Checking performance on the training set

confusion_matrix_sklearn(best_model2, X_train, y_train)

# calculation of the scores
decision_tree_postpruned_perf_train = get_recall_score(best_model2, X_train, y_train)
print("Recall Score:", decision_tree_postpruned_perf_train)

Recall Score: 0.7746024154011718

Checking performance on the test set

# creating the confusion matrix
confusion_matrix_sklearn(best_model2, X_test, y_test)

# calculation of the scores
decision_tree_postpruned_perf_test = get_recall_score(best_model2, X_test, y_test)
print("Recall Score:", decision_tree_postpruned_perf_test)

Recall Score: 0.7731402612152186

• The results have improved from the initial model.
• The performance is comparable to the hyperparameter tuned model.
• The model is giving a generalized performance on training and test set.

Visualizing the Decision Tree

# plotting the decision tree
plt.figure(figsize=(15, 10))

out = tree.plot_tree(
    best_model2,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=True,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()

# Text report showing the rules of a decision tree -

print(tree.export_text(best_model2, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- weights: [1251.36, 239.19] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- weights: [271.26, 284.08] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- weights: [358.05, 219.76] class: 0
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- weights: [468.27, 1634.80] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- weights: [2819.19, 804.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- weights: [422.40, 998.97] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- weights: [29.04, 1422.41] class: 1

Observation

• If a booking is made with a lead time of less than 90.5 days, there are no special request and it's not made online, there is a likelihood that the booking will be kept.

• If a booking is made with a lead time of less than 151.5 day and special request are made at the time of booking and it's online, there is also a likelihood that the booking will be kept.

• If a booking is made with a 151.5 days > lead time >13.5 days , there are no special requests and it is made online, there is a likelihood of the booking being Cancelled.

# importance of features in the tree building ( The importance of a feature is computed as the
# (normalized) total reduction of the 'criterion' brought by that feature. It is also known as the Gini importance )

print(
    pd.DataFrame(
        best_model2.feature_importances_, columns=["Imp"], index=X_train.columns
    ).sort_values(by="Imp", ascending=False)
)

                                           Imp
lead_time                             0.568596
market_segment_type_Online            0.217324
no_of_special_requests                0.159728
avg_price_per_room                    0.054353
no_of_weekend_nights                  0.000000
room_type_reserved_Room_Type 6        0.000000
required_car_parking_space_1          0.000000
room_type_reserved_Room_Type 2        0.000000
room_type_reserved_Room_Type 3        0.000000
room_type_reserved_Room_Type 4        0.000000
room_type_reserved_Room_Type 5        0.000000
arrival_year_2018                     0.000000
room_type_reserved_Room_Type 7        0.000000
type_of_meal_plan_Meal_Plan_3         0.000000
market_segment_type_Complementary     0.000000
market_segment_type_Corporate         0.000000
market_segment_type_Offline           0.000000
type_of_meal_plan_Not_Selected        0.000000
no_of_children_10                     0.000000
type_of_meal_plan_Meal_Plan_2         0.000000
no_of_week_nights                     0.000000
no_of_children_9                      0.000000
no_of_children_3                      0.000000
no_of_children_2                      0.000000
no_of_children_1                      0.000000
no_of_adults_4                        0.000000
no_of_adults_3                        0.000000
no_of_adults_2                        0.000000
no_of_adults_1                        0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
arrival_date                          0.000000
arrival_month                         0.000000
repeated_guest_1                      0.000000

importances = best_model2.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

Model Performance Comparison and Conclusions

# training performance comparison

models_train_comp_df = pd.DataFrame(
    [
        decision_tree_perf_train,
        decision_tree_tune_perf_train,
        decision_tree_postpruned_perf_train,
    ],
    columns=["Recall on training set"],
)

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Recall on training set
0	0.995097
1	0.904819
2	0.774602

# testing performance comparison

models_test_comp_df = pd.DataFrame(
    [
        decision_tree_perf_test,
        decision_tree_tune_perf_test,
        decision_tree_postpruned_perf_test,
    ],
    columns=["Recall on testing set"],
)

print("Test performance comparison:")
models_test_comp_df

Test performance comparison:

	Recall on testing set
0	0.803805
1	0.865701
2	0.773140

data = {
    "Logistic Regression Default Threshold": log_reg_model_test_perf.T.loc["Recall"][0],
    "Logistic Regression-0.37 Threshold": log_reg_model_test_perf_threshold_auc_roc.T.loc[
        "Recall"
    ][
        0
    ],
    "Logistic Regression-0.41 Threshold": log_reg_model_test_perf_threshold_curve.T.loc[
        "Recall"
    ][0],
    "Original_Pruned Tree": [decision_tree_perf_test],
    "Pre-Pruned": [decision_tree_tune_perf_test],
    "Post_Pruned": [decision_tree_postpruned_perf_test],
}

models_test_comp_df = pd.DataFrame(data, index=["Recall Values"])


print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:

	Logistic Regression Default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.41 Threshold	Original_Pruned Tree	Pre-Pruned	Post_Pruned
Recall Values	0.633163	0.737649	0.702726	0.803805	0.865701	0.77314

Observations

• The Logistic Regression- 0.32 Threshold (Optimized ROC Curve) - gave the highest recall of the Logistic Regression Models

• While the Pre-Pruned Tree gave the highest recall on the test data, the tree is too complex

• The Post-Pruned Tree created a manageable tree with a Recall of 0.77 which is higher than the best Logistic Regression Model(i.e. the Logistic Regression Model with 0.32 threshold)

Answers to questions

What are the busiest months in the hotel?

• The busiest months in the hotel based on booking percentage are :
  • October -14.7 %
  • September - 12.7%
  • August - 10.5%
  • June - 8.8%

Which market segment do most of the guests come from?

• Based on the plot above :
  • 64% of the bookings are Online
  • 29% of the bookings are Offline
  • 5.6% of the bookings are from Corporate
  • 1.4% are from Aviation

Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?

• Based on the plot above the average room prices per market segment are

  • Online - 112 euros
  • Aviation - 101 euros
  • Offline - 92 euros
  • Complementary - 3 euros

• As we can see Online bookings are the most expensive. As a result, since 71.3 % of the cancelled bookings were from the Online sector, this would have a significant impact on revenue.

What percentage of bookings are canceled?

• Based on the plot above, we can see that 32.8% of the bookings are cancelled.

• Additionally, we can see from the plot below that over 71.3% of the 11885 bookings that were cancelled comes from Online. Hence the need for a strict policy w.r.t cancellation of Online bookings.

Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?

## Here we find the number of repeated guest that have canceled by applying a filter on the original data set
Cancellled_bookings = INN[INN["booking_status"] == "Canceled"]
Cancelled_repeat = Cancellled_bookings[Cancellled_bookings["repeated_guest"] == 1]
Total_repeated_guest =INN[INN["repeated_guest"]==1]
percentage = round((len(data10["repeated_guest"]) / len(Total_repeated_guest["repeated_guest"])) * 100, 2)
print("The percentage of repeated guest that canceled is :", percentage,"%")

The percentage of repeated guest that canceled is : 1.72 %

Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?

Cancellled_bookings = INN[INN["booking_status"] == "Canceled"]
labeled_barplot(Cancellled_bookings, "no_of_special_requests", perc=False)

Observations

• Of the 11885 cancelled bookings;
• 71.9% consists of bookings that have no special requests attached to them.
• 22.7% consists of bookings that have 1 special requests attached to them.
• 5.4% consists of booking that have 2 special requests attached to them.

• So we can clearly see that there is a link between special requirements and booking cancellations. Bookings without special requirements should be a red flag to the hotel management, since there is a high probability that they can be cancelled.

Actionable Insights and Recommendations

• What profitable policies for cancellations and refunds can the hotel adopt?
• What other recommedations would you suggest to the hotel?

Decision Tree Summary

• The important features from most important to least are 1) lead time, 2) market segment type Online, 3) no of special request and 4) average price per room.

Scenario 1 - If the booking lead time is less than 5 months (151 days), in this scenario , the deciding factors for a cancellation or not cancelled are

• Number of special request and
• The Market segment that placed the order (whether it is online or not)

• If there are no special requests and it is not an online booking

  • If the lead time is less than 3 months, the booking is expected to be kept.
  • If the lead time is > 3months, the booking is expected to be cancelled.

• If there are no special requests are made and it is an online booking

  • If the lead time is less than 13.5 days the booking is expected to be kept
  • If the lead time is more than 13.5 days, the booking is expected to be cancelled

• Policy for Scenario 1, will be to place a high penalty for cancellation
  • If a booking is made from other segments with a lead time of less than 3 months.- (Since we are booking revenue on this as it's a booking that's expected to be kept)
  • If a booking is made online with a lead time of less than 13.5 days- (Since we are booking revenue on this as it's a booking that's expected to be kept)

Scenario 2 - If the lead time is less than 5 months, special request are made, it doesn’t matter what segment the booking was made from, it is expected to be kept.

• Policy for Scenario2, will be to place a high penalty for cancellation
  • If the lead time is less than 5 months and special request are made. - (Since we are booking revenue on this as it's a booking that's expected to be kept)

Scenario 3 - If the lead time is > 5 months whether or not the price of the room is greater or less than 100 euros, there is a likelihood that the room can be cancelled.

• No policy is required for Scenario 3, since if the lead time is more than 5 months, even if the booking is cancelled, there is sufficient time for the booking to be fulfilled.

Regression Model Summary

Based on the regression there were some factors such as being a repeated guest, having special request, making the booking through offline or corporate and requiring parking space that decreased the odds of a booking being cancelled.

Some of these were consistent with the decision tree that indicated for example, if lead time was less than 5 months, granted special request were made, the booking was expected to be kept regardless of the method of booking.

Also with the decision tree, if a booking is made through other segments except online and the lead time is less than 3 months, the booking was expected to be kept. This is consistent with the regression model which indicated that there was a 83% and 52% decrease in odds of a booking being cancelled if the booking was made Offline or Corporate respectively. Based on the intersection between the results of the Decision Tree and the logistic regression model, the following Policy for Cancellation and Refunds seems appropriate.

Policy Summary

Cancellations

Cancellation Penalty fees will be applied in the following situations

* If a booking is made from other segments (excluding Online) with a lead time of less than 3 months and there are no special request.
* If a booking is made online with a lead time of less than 13.5 days and there are no special request 
* If the lead time is less than 5 months and special request are made, regardless of if the booking was made online or not.

Penalty fees do not apply if bookings are cancelled more than 151 days (5 months in advance)

Refunds

• Full Refunds will only be issued if any bookings are cancelled more than 5 months in advance
• If bookings are cancelled less than 5 months but it is still greater than 3 months, provided it is not an Online booking , only 50% refund will be issued.
• If bookings are cancelled 14 days or less from due arrival date, there will be no refund.